Find Your Dream Python Job in India

Role : Python Backend Developer (SDE-II)

Location : Bangalore (Onsite) 5 days working

Experience : 5+ years

Budget : 15 - 18 LPA

Qualification : Bachelor's or master's degree in computer science, Engineering, or a related technical field.

Senior Backend Developer (SDE-II) is an impactful role in the design, development, and deployment of software solutions. Further, will be expected to collaborate with a team of engineers and stakeholders, solving complex technical challenges while driving best practices in engineering and operational excellence.

Mandatory Skills: Python, Django , web application development.

Key Responsibilities:

• Design & Development: Own the design, development, and delivery of scalable and performant software systems, working across the entire software development lifecycle (SDLC).
• Problem Solving: Analyze user requirements, design system architectures, and create efficient, reliable, and reusable code.
• Code Quality & Best Practices: Write high-quality, maintainable, and efficient code while adhering to coding standards, and perform peer code reviews.
• Testing & Automation: Design and implement robust test cases (unit, integration, performance), and automate testing wherever possible.
• Systems & Architecture: Contribute to the architectural decisions to build large-scale distributed systems, ensuring that they are scalable, secure, and cost-effective.
• Collaboration: Work closely with other engineers, front end developers, product managers, and stakeholders to clarify requirements, define solutions, and deliver results on time.
• Documentation: Maintain detailed documentation for backend systems, including API documentation, complex solutions, data models, and third-party system integrations.
• Mentoring & Leadership: Mentor junior engineers, helping them improve their technical skills and code quality, while fostering a collaborative and inclusive team environment.
• Continuous Improvement: Stay updated with the latest technologies, industry trends, and best practices, and bring fresh ideas to the team to improve efficiency, performance, and scalability.

Domain/Technical Understanding/ Additional Skills

• Proficiency in one or more programming languages - Java or Python and a deep understanding of data structures, algorithms, and system design.
• Strong problem-solving skills and the ability to tackle challenging technical problems independently.
• Experience working with cloud services like AWS, Azure, or GCP.
• Expertise in microservices architecture, API development, and database design (SQL and NoSQL).
• Experience with DevOps, CI/CD pipelines, and automation of deployment processes.
• Strong understanding of agile methodologies and collaborative team environments.
• Knowledge of security best practices for software development.
• Strong written and verbal communication skills.
• Ability to work independently and in cross-functional teams.
• Supply Chain Domain

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Job Summary

As a Technical Lead , you will serve as the architectural backbone and technical visionary for our backend engineering team. This role is designed for a high-impact individual who balances deep hands-on expertise in Python and GCP with the leadership skills necessary to guide a team through complex technical challenges. You will not only write mission-critical code but also define the technical roadmap, ensure system scalability, and serve as the primary technical point of contact for stakeholders and clients.

Key Responsibilities

️ Technical Leadership & Architecture

• Architectural Ownership: Lead the design and implementation of high-performance, scalable, and secure backend systems using Python (FastAPI/Flask).
• GCP Strategy: Drive the adoption of best practices within the Google Cloud Platform ecosystem (GKE, Cloud Run, Pub/Sub, Cloud Spanner/SQL).
• Technical Roadmap: Propose and execute architectural changes that improve system resilience, reduce technical debt, and support long-term business goals.
• Security & Standards: Establish and enforce security best practices and coding standards across the entire backend stack.

Team Mentorship & Delivery

• Lead by Example: Act as the technical lead for the engineering team, providing high-level guidance while remaining "hands-on" with complex features and debugging.
• Code Excellence: Oversee the code review process to ensure quality, maintainability, and adherence to CI/CD standards.
• Talent Development: Mentor junior and mid-level engineers, fostering a culture of continuous learning and technical growth.
• Process Optimization: Refine CI/CD pipelines and DevOps integration to streamline the development-to-production lifecycle.

Stakeholder & Project Management

• Technical Point of Contact: Serve as the primary liaison between the engineering team and product managers, designers, and clients.
• Solutioning: Translate complex business requirements into clear technical specifications and documentation.
• Strategic Collaboration: Partner with cross-functional teams to ensure the timely delivery of features that meet both user needs and technical constraints.

Required Skills & Qualifications

Technical Expertise

• Experience: 12+ years of professional experience in software development with a heavy focus on Python.
• Frameworks: Mastery of FastAPI (highly preferred) or Flask for building high-concurrency RESTful APIs.
• Cloud Infrastructure: Hands-on experience architecting and deploying applications on Google Cloud Platform (GCP).
• Testing & Quality: Expert-level knowledge of unit testing, integration testing, and automated quality assurance.
• Engineering Excellence: Strong grasp of Data Structures, Algorithms, and System Design patterns.

Leadership & Soft Skills

• Communication: Exceptional ability to articulate technical concepts to non-technical stakeholders.
• Problem Solving: A "first-principles" approach to troubleshooting and resolving critical production issues.

Bonus Skills (Optional)

• Proven Leadership: Experience acting as a Technical Lead or Lead Developer for a team of 4+ engineers.
• Frontend Literacy: Familiarity with React, Angular, or Vue.js to better collaborate with UI/UX teams.
• Infrastructure as Code: Experience with Terraform or Pulumi.
• Orchestration: Deep knowledge of Docker and Kubernetes (GKE).

About the Role

Build Python-powered enterprise-grade scalable backend systems and APIs. Own the end-to-end architecture for application and product development, drive Agile delivery, and serve as the primary technical authority. Translate evolving business requirements into robust, well-documented backend systems that power multi-user enterprise products - from database design through deployment.

You will excel if you:

• Think in systems and architecture - designing APIs, services, and databases that scale

• Lead with a product mindset - translating business goals into technical roadmaps

• Drive quality standards through code reviews, testing, and documentation

• Mentor and elevate team members through structured technical guidance

Key Responsibilities

Backend Architecture

• Lead end-to-end development of scalable backend systems, APIs, and web applications using Python (Django/Flask/FastAPI), with deep focus on database design and service architecture.

• Design modular, maintainable codebases following OOP, microservices, and established design patterns.

• Define reusable patterns for data-intensive operations: async processing, event-driven workflows, and real-time data handling.

• Architect REST and GraphQL APIs with clear contracts, versioning, and documentation standards.

Technical Leadership

• Guide the team as Tech Lead: conduct code reviews, mentor juniors, enforce enterprise coding standards, and promote engineering best practices.

• Own the technical roadmap: surface architectural gaps, trade-offs, and improvement areas before they become blockers.

• Collaborate with product, front-end, QA, and DevOps teams for CI/CD, Docker deployments, and cloud integration (AWS/Azure/GCP).

• Serve as the primary decision-maker on backend technology choices, library selection, and architectural patterns.

Product & Agile Delivery

• Apply product strategy thinking to align technical decisions with business goals, prioritizing features via Agile sprints, backlogs, and iterative delivery.

• Drive Agile ceremonies: sprint planning, stand-ups, and retrospectives; ensure delivery velocity without compromising code quality.

• Translate evolving requirements into implementation-ready technical specifications before development begins.

Data & Performance

• Optimize SQL/NoSQL databases (PostgreSQL/MySQL/MongoDB/Redis): query performance, indexing strategy, and schema design.

• Implement async programming patterns (Celery, asyncio) to handle high-throughput, background task, and event-driven workloads.

• Troubleshoot production issues: root cause analysis, profiling, and systematic performance improvements.

Quality & Documentation

• Enforce high-quality, secure code with unit and integration testing (PyTest) as a non-negotiable standard.

• Maintain developer-facing documentation: API references, architecture decision records, and onboarding guides.

• Own CI/CD pipeline standards: automated testing, linting, and deployment validation before every release.

Required Technical Knowledge

• 5+ years hands-on Python 3.x with proven backend expertise in Django, Flask, or FastAPI at enterprise scale.

• Architecture: REST/GraphQL API design, microservices, event-driven systems, and scalable DB schema design.

• Databases: PostgreSQL, MySQL, MongoDB, Redis - query optimization, indexing, and data modelling.

• Infrastructure: Docker, Git, CI/CD pipelines, and cloud platforms (AWS/Azure/GCP).

• Testing: PyTest, unit and integration testing, and code coverage standards.

• Team leadership: code reviews, mentoring, and enterprise coding standards enforcement.

• Agile/Scrum: sprint ceremonies, backlog management, and product-led iterative delivery.

Good to Have

• Analytics mindset: Pandas, NumPy for data insights and feature optimization.

• Front-end awareness (React/Angular) for effective cross-functional collaboration.

• Real-time patterns: WebSockets, Kafka, RabbitMQ for streaming and messaging workloads.

• AI/ML exposure (TensorFlow, scikit-learn) for data-driven feature development.

• Experience with SaaS or enterprise multi-tenant application architecture.

Key Deliverables

• Production-ready scalable backend systems and API libraries

• Technical architecture documentation and API specification ecosystem

• Code review standards, testing coverage reports, and quality audit records

• Structured onboarding documentation and developer-facing README specifications

Qualifications

• Bachelor's/Master's in Computer Science, IT, or related field.

• 5+ years in Python backend development with demonstrated Tech Lead experience.

What Success Looks Like

• Scalable, maintainable backend systems that require zero architectural rewrites at growth

• Evolving requirements converted into structured technical specs with no back-and-forth

• A team that ships confidently with high test coverage and clean, documented code

• Backend becomes the stable, trusted foundation that all product features are built on

Job Category: Contract / Project-based

Job Type: Full Time

Job Location: Chennai

Company Description

Preface Consulting is a leading international consulting firm established in 2017, with extensive operations in India and the UAE. Specializing in Finance, Indirect Taxation, and Global Trade Compliance, the company provides end-to-end solutions for GST, VAT, corporate tax, customs advisory, and import/export processes. Using advanced technology, including its proprietary Preface Facilitation Platform, and a team of skilled professionals, Preface Consulting enhances client outcomes through AI-driven insights and efficient data analysis. The firm also delivers business finance management services, helping organizations improve cash flow, optimize processes, and achieve sustainable growth. Headquartered in key business hubs, Preface Consulting is committed to driving operational excellence and regulatory compliance for its global clientele.

Location: Bengaluru / Trivandrum

About the Role

We are looking for a Backend Developer with strong expertise in Python and working knowledge of Node.js / NestJS to join our team. You will be responsible for building scalable backend systems, developing high-performance APIs, and managing database-driven applications.

Key Responsibilities

• Develop and maintain backend services primarily using Python (FastAPI/Django or similar frameworks)

• Design and build scalable REST APIs

• Work with Node.js / NestJS for selected services and integrations

• Design, implement, and optimize PostgreSQL databases

• Write efficient SQL queries and handle database performance tuning

• Collaborate with frontend, product, and DevOps teams

• Ensure code quality, scalability, and maintainability

Requirements

• Strong experience in Python backend development

• Working knowledge of Node.js and basic familiarity with NestJS

• Solid understanding of SQL and PostgreSQL

• Experience with API design and backend architecture

• Understanding of performance optimization and scalability

• Familiarity with Git and version control workflows

Good to Have

• Experience with FastAPI or Django

• Exposure to microservices architecture

• Knowledge of async programming and background processing

• Basic understanding of AI/ML integrations

Experience

• 1-3 years of backend development experience (or strong project portfolio)

TCS Hiring for Python+Informatica+SQL

Experience Range: - 4 to 9 Years

Location: Chennai/Kolkata/Bangalore

Notice Period: Immediate Joiners or NP less than 45 days

Candidates with experience in Python Automation, Informatica, SQL database management and AWS preferred.

• Strong hands-on expertise in Informatica Cloud (IICS) and Informatica PowerCenter.

• Python skills with active involvement in IICS Python migration and automation work.

• Advanced SQL proficiency across Snowflake, SQL Server, and Oracle.

• Experience with Unix shell scripting for automation, file processing, and workflow orchestration.

• Strong understanding of data warehousing concepts, dimensional modelling, and large-scale data integration.

Technical Expertise

• End-to-end ETL development including design, development, testing, deployment, and support.

• Troubleshooting complex ETL session failures, batch issues, and data quality inconsistencies.

• Performance tuning for ETL workflows, SQL queries, and transformation logic.

• Hands-on experience in large-scale data migration ensuring accuracy, consistency, and reliability.

• Contributed to modernization efforts including Informatica on-prem to IICS migration and Python-based re-engineering.

• Experience building secure, scalable data pipelines supporting BI and reporting systems.

Good to have Skills

• Hands-on experience with Python-based ETL frameworks or automation scripts.

• Familiarity with cloud data ecosystems and CI/CD practices for data pipelines.

• Knowledge of modern data engineering tools and cloud platform capabilities.

Key Responsibilities

• Designed and developed ETL workflows using Informatica Cloud (IICS) and PowerCenter for enterprise data integration needs.

• Built secure, scalable data pipelines to load data from multiple source systems into a unified data warehouse.

• Implemented complex data transformations and financial domain logic to ensure accurate and consistent data delivery.

• Managed large-scale data migration processes, maintaining high data quality and integrity across all phases.

• Identified and resolved ETL session failures, batch issues, and data processing errors to minimize operational downtime.

• Performed performance tuning on ETL workflows and SQL queries to improve throughput and reduce processing time.

• Delivered end-to-end solution support, including development, implementation, testing, deployment, and production maintenance.

• Contributed to Informatica on-premise to cloud (IICS) migration initiatives to streamline and modernize ETL operations.

• Supported ongoing IICS to Python migration, exploring automation and AI-driven enhancements in data engineering. Collaborated closely with consultants, clients, and stakeholders to align requirements and ensure smooth project execution

"Looking for immediate joiners and notice period with either 45days or less than 45 days"

Enterprise Lead Architect

enGen Global is an emerging global healthcare partner that delivers strategic innovation, expertise, and flexibility to its healthcare partners. Being a US healthcare conglomerate captive, we have direct access to deeper insights that help us accelerate our learning process and keeps us ahead of the curve. enGen Global delivers next-generation solutions that enable our healthcare partners to provide positive experiences to their consumers.

Our global collaborative of healthcare, operations, and IT experts creates innovative and sustainable processes for our clients, which keeps the ever-evolving consumers engaged and assists them in managing the future of their healthcare better.

About the AI Platforms and Services (AIPS) Team:

The AI Platforms and Services (AIPS) team is a critical enabler of AI-driven innovation within the company. We function as an internal service provider, delivering cutting-edge AI solutions and infrastructure to various business units and data science teams. Our mission is to empower the organization to leverage AI effectively, improve processes, and develop new AI-powered applications.

Our team culture is highly collaborative, innovative, and results oriented. We value continuous learning, open communication, and a commitment to excellence. We foster a supportive environment where team members can grow their skills and contribute to impactful projects. The team works closely with internal stakeholders to understand their needs and deliver tailored AI solutions. We also prioritize staying at the forefront of AI advancements, continuously exploring new technologies and methodologies.

Role Summary:

We are seeking an experienced Enterprise Architect to lead system design and solution architecture across a diverse set of enterprise AI initiatives. This role is ideal for someone with a strong software engineering background, deep experience in Python, and a solid understanding of enterprise integrations. Familiarity with AI solutioning is a plus, but not required.

You will collaborate with cross-functional teams to design scalable, secure, and maintainable systems that support our enterprise AI platform and Services initiatives. The Enterprise Architect plays a key role in translating business needs into AI-based technical solutions that align with strategic goals and enable long-term success across the organization.

Responsibilities:

• Lead end-to-end system design and solution architecture for enterprise-grade applications and platforms, with a focus on scalability, security, and maintainability.

• Drive the development and integration of AI-enabled solutions into existing enterprise platforms, supporting strategic AI platform and services initiatives.

• Collaborate with engineering, product, and business teams to translate requirements into actionable technical solutions.

• Design and implement enterprise integrations using APIs, messaging systems, and data pipelines across complex, heterogeneous environments.

• Evaluate and recommend technologies, frameworks, and tools that align with business goals and technical strategy.

• Ensure solutions comply with security, scalability, reliability, and regulatory standards, including HIPAA and other Healthcare IT requirements.

• Work closely with business stakeholders to understand needs and contribute to the strategic direction of AI and enterprise architecture initiatives.

• Provide technical guidance and mentorship to project teams, fostering innovation, excellence, and continuous improvement.

• Lead proof-of-concept (POC) efforts to validate the feasibility and value of proposed solutions.

• Collaborate with data scientists, engineers, and technical experts to ensure successful execution of projects.

• Develop and maintain comprehensive architecture documentation, including diagrams, specifications, and implementation guidelines.

• Communicate complex technical concepts effectively to both technical and non-technical audiences.

• Stay current with advancements in AI, cloud, and enterprise technologies, proactively identifying opportunities for innovation.

• Partner with US and enGen Global leaders to provide project status updates, risk assessments, and strategic recommendations.

Skills:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field.

• 10+ years of experience in software development, solution architecture, or enterprise systems design.

• Strong programming skills in Python, with experience building scalable, production-grade systems.

• Proven experience designing and implementing enterprise integrations

• Hands-on experience with cloud platforms such as AWS, Azure, or GCP - with a strong preference for Google Cloud Platform (GCP) and its AI-related services.

• Excellent communication and presentation skills, with the ability to convey complex technical concepts to both technical and non-technical audiences.

• Demonstrated ability to lead technical discussions, influence decision-making, and drive enterprise initiatives to successful outcomes.

• Experience working with cross-functional teams and stakeholders, with a strong ability to collaborate and build consensus.

• Strong analytical and problem-solving skills, with a track record of addressing complex technical challenges.

• Leadership experience, including providing technical guidance and mentorship to engineering and project teams.

Good to have skills:

• Knowledge of healthcare industry standards and regulations (e.g., HIPAA) is required, as well as a strong understanding of the Healthcare IT landscape.

• Experience with MLOps practices and tools, with a focus on automating and streamlining the AI development and deployment lifecycle.

• Preferred experience in designing and implementing scalable and robust AI solutions within the Healthcare IT domain.

Agile methodology

Shift Requirement:

3PM to 12 midnight (open to morning shift for the right candidate)

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Summary

A client of Coretek Labs is immediately hiring for a Python AI Engineer - RAG Pipelines & Autonomous Agents

Title: Python AI Engineer - RAG Pipelines & Autonomous Agents

Position type: Full Time/ Contract

Location: Hybrid/Remote

Python AI Engineer - RAG Pipelines & Autonomous Agents, You will:

Generative Agentic AI Engineering

• Build and optimize LLM driven autonomous agents, multi agent systems, and tool using workflows.
• Develop Model Context Protocol (MCP) servers and structured context?management frameworks.
• Architect scalable RAG pipelines (embeddings, vector search, retrieval layers, grounding strategies, prompt engineering).
• Implement LLM function calling, multi step orchestration, guardrails, evaluation frameworks, and safety/quality controls.

Python Enterprise AI Engineering

• Build high performance Python microservices and AI APIs using FastAPI, Flask, LangChain, LlamaIndex, and MCP SDKs.
• Engineer distributed AI systems for large scale inference, retrieval, and multi agent orchestration.

Technologies Ecosystem

• Use Jupyter Notebooks, Tachyon, and enterprise GenAI platforms for experimentation and model refinement.
• Leverage GitHub Copilot and modern DevSecOps workflows to accelerate development.
• Contribute to reusable AI patterns, enterprise accelerators, and Responsible AI guardrails.

Cloud Platform Engineering

• Deploy and operate AI solutions on Google Cloud Platform (GKE, Vertex AI, Cloud Run, IAM).
• Containerize and orchestrate AI services using Red Hat OpenShift and enterprise Kubernetes.
• Build and manage CI/CD pipelines aligned to DevOps best practices.

Data Integration & Retrieval

• Work with vector databases (MongoDB Atlas Vector Search, Chroma, Pinecone, Redis, pgVector).
• Build secure, scalable retrieval layers, embedding pipelines, and long term AI memory modules.

Architecture, Governance & Delivery

• Participate in architecture reviews and contribute to compliant AI governance frameworks.
• Ensure adherence to risk, security, and regulatory standards.
• Lead POCs, engineering improvements, and innovation workstreams with minimal oversight.

The ideal candidate will have:

• Experience building LLM generative AI or agentic AI systems.
• Experience in Python (async programming, APIs, microservices, distributed systems).
• Experience with GCP and OpenShift/Kubernetes for scalable AI deployments.
• Experience with RAG pipelines, embeddings, vector search, and LLM orchestration.
• Experience with Jupyter, Tachyon, GitHub Copilot, CI/CD, and modern DevOps tooling.
• Demonstrated ability to work independently and drive AI innovation.
• Familiarity with LangChain, LlamaIndex, and APIs for OpenAI, Google Gemini, or similar models.
• Experience with enterprise observability stacks (Grafana, Cloud Logging, Prometheus).

Ascendion is looking for an experienced Python Data Engineer who is working on data platforms and who has worked on big-data platforms projects.

Required Skills & Experience:

Must have/non-negotiable skills:

• Minimum of 7+ years of total relevant experience required.
• Python programming with python libraries experience (Pandas, NumPy, and PySpark). Python scripting profiles will not work here.
• Azure cloud experience.
• Database expertise is crucial – advanced SQL, data modelling, and query optimization are key requirements.
• Cloud Platforms: Experience with major cloud providers (Azure is must) and their data services (e.g., S3, Redshift, BigQuery).
• Communication: Ability to explain technical infrastructure and data lineage to non-technical stakeholders.

About Us:

Ascendion is a leading provider of AI-first software engineering services. Our applied AI, software engineering, cloud, data, experience design, and talent transformation capabilities accelerate innovation for Global 2000 clients. Ascendion is headquartered in New Jersey. In addition to our remote/hybrid workforce, we have 30+ offices across the U.S., UK, Poland, Romania, India, Australia and Mexico. We are committed to building technology powered by Generative AI with an inclusive workforce, service to our communities, and a vibrant culture. For more information, please go to

Job Title - QA / Automation Engineer

Job Location - Pune, Maharashtra

MUST Have Skills - Python, Writing Automation Test Scripts, Security testing / Cybersecurity domain

Position Overview

As a Automation Engineer, you will have end-to-end ownership of the quality assurance of relevant software components of our cybersecurity platform. You will play a crucial role in implementing robust quality assurance practices and standards to ensure the reliability, accuracy, and security of automated tests, deployment processes, and our SaaS platform. Collaboration with cross-functional teams, including development, UI, DevOps, IT/Ops, and non-engineering stakeholders, is integral to this role.

Responsibilities

• Collaborate with the engineering leadership team to develop and execute a comprehensive automation strategy that aligns with our product roadmap and business objectives.
• Contribute to the development, implementation, and maintenance of automated testing frameworks, CI/CD pipelines, and deployment scripts to streamline the software development lifecycle.
• Work on design and architecture of automation solutions, considering factors such as scalability, performance, and security requirements.
• Develop automated security testing tools and processes to identify vulnerabilities, assess risks, and ensure compliance with industry standards and regulations (e.g.

Roles & Responsibilities:

• Should have good hands-on experience with Python Development.
• Should be able to understand the test infrastructure and Test Framework.
• Should have hands-on experience with Robot framework to test transactional analysis based systems.
• Good knowledge of the OOPS concept.
• Should have hands-on experience on building Automation Framework.
• Good Analytical and Problem-Solving skills.
• Exposure to Shell scripting.
• Quick Debugging skills of test execution errors and completely responsible for resolving the issues independently.
• Understanding of the existing framework, maintaining the new script addition, and proposing enhancements to the framework.
• Enhancement of new ideas, and framework development to make the framework more robust.
• Should be able to work independently with no or minimum supervision.

Experience:

• Understanding of system architecture.
• Good understanding of Server domain.
• Experience of test development with positive and negative test scenarios.
• Should have good knowledge on Linux & Windows OS.
• Experience in Application, API automation desired.

Hiring: Mobile Automation Engineer

Location: Bangalore

Experience: 5+ Years

Joining: Immediate Joiners Preferred

Interview Process: Face-to-Face Discussion

We are looking for a skilled Mobile Automation Engineer with 5+ years of experience to join our team in Bangalore. If you are passionate about automation testing and ready to take on an exciting opportunity, we’d love to connect with you!

Key Skills Required:

• Strong experience in Mobile Automation Testing
• Hands-on experience with Appium
• Good knowledge of Selenium / WebDriver
• Experience in Android & iOS testing
• Strong understanding of Java / Python
• Experience with CI/CD tools (Jenkins, Git, etc.)
• Knowledge of API Testing is an added advantage
• Good communication and problem-solving skills

Work Location: Bangalore

Interview Mode: Face-to-Face

If interested, kindly share your updated CV to:

sneha.p@acldigital.com

Immediate joiners will be given preference.

About Talent500:

Talent500 is the go-to premium destination for the best global job opportunities at Global Capability Centres or GCCs in India.

We believe in opportunities favoring the bold and thus, we help the best tech and non-tech talent find their dream jobs at renowned companies that leads to a transformative experience career wise. Talent500 is a part of ANSR. ANSR Global is trusted by industry leaders across the globe – from Fortune 500 companies and larger enterprises to some of the world’s fastest growing Startups.

Experience for the role: 5-12 years

Responsibilities:

• Design and develop scalable and reliable backend systems using Java and Microservices architecture
• Build and maintain RESTful APIs and Microservices using Spring Boot
• Ensure the performance, security, and availability of the backend systems
• Collaborate with frontend developers to integrate the backend systems with the user interface
• Troubleshoot and debug issues, and perform root cause analysis
• Write and maintain technical documentation
• Keep up-to-date with the latest technologies and industry trends

Requirements:

• Strong experience in Java and Microservices using Spring Boot
• Experience with RESTful APIs and developing scalable systems
• Knowledge of database technologies such as MySQL, PostgreSQL, or MongoDB
  

  Talent500

  

  3 days ago